Radar technical language modeling with named entity recognition and text classification

Jackson S. Zaunegger, Paul G. Singerman, Ram M. Narayanan, Sean M. O'Rourke, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper introduces the radar text data set (RadarTD) for technical language modeling. This data set is comprised of sentences containing radar parameters, values, and units determined from real-world values. This data set is created based on values determined from published academic research. Additionally, each statement is assigned a sentiment label and goal priority label. Preliminary investigations into the applicability of this data set are explored using the BERT model and several bi-directional LSTM models. These models are evaluated on text classification and named entity recognition tasks. This study evaluates the applicability of technical language modeling using neural networks to analyze input statements for cognitive radar applications. These findings suggest that this data set can be used to achieve reasonable performance for both text classification and named entity recognition for autonomous radar applications.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXVI
EditorsKenneth I. Ranney, Ann M. Raynal
PublisherSPIE
ISBN (Electronic)9781510650923
DOIs
StatePublished - 2022
EventRadar Sensor Technology XXVI 2022 - Virtual, Online
Duration: Jun 6 2022Jun 12 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12108
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceRadar Sensor Technology XXVI 2022
CityVirtual, Online
Period6/6/226/12/22

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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